Video Reflection Detection Using Behavioral Regime Correlation
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Solution Overview
Problem
Reflections in video analytics systems lead to false reporting and misidentification of objects, as systems treat reflections as separate entities, causing inaccuracies in object counting and behavior analysis.
Innovation Solution
A device tracks objects in video data, representing their spatial characteristics over time as timeseries and associates these with behavioral regimes to detect reflections by correlating changes in these regimes, using pairwise correlations and heatmaps to distinguish between real objects and their reflections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video analytics system tracks all detected objects, then object tracking coverage is improved, but false reporting increases due to reflections being misidentified as separate objects
Solution Approach 1:
The patent introduces an intermediary reflection detection module that acts as a mediator between object detection and tracking. This module analyzes video frames to identify reflective surfaces and determines whether detected objects are reflections of other objects. By inserting this intermediary layer, the system can filter out false object detections caused by reflections while maintaining comprehensive tracking of real objects, thus resolving the contradiction between detection coverage and reporting accuracy.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously analyzed and fed back into the tracking process. The reflection detection module provides feedback about identified reflections to the object tracker, which then adjusts its behavior accordingly. This feedback loop enables the system to maintain high detection coverage while eliminating false reports from reflections, as the tracker can dynamically respond to reflection information.
2Quantity of substance
If video analytics system identifies all detected entities as separate objects, then object detection completeness is improved, but object counting accuracy deteriorates due to duplicate counting of reflections
Solution Approach 1:
The reflection detection module serves as an intermediary that analyzes detected objects to distinguish real objects from reflections. It examines video frames, identifies reflective surfaces, and determines reflection relationships between objects. This intermediary analysis enables the system to maintain complete detection of all entities while accurately counting only real objects by filtering out reflections, thus resolving the contradiction between detection completeness and counting accuracy.
Solution Approach 2:
The patent segments the object detection process into distinct phases: initial object detection, reflection detection and analysis, and final object confirmation. By dividing the detection process into these segments, the system can first detect all potential objects comprehensively, then separately analyze which ones are reflections, and finally confirm only real objects for counting. This segmented approach maintains detection completeness while ensuring counting accuracy.
3Adaptability or versatility
If video analytics system analyzes behavioral patterns of all detected objects, then event detection capability is improved, but false behavior analysis increases due to reflections being treated as independent entities
Solution Approach 1:
The reflection detection module acts as an intermediary between object detection and behavior analysis. It identifies reflections before the behavior analysis phase, providing this critical information to the event detection system. This allows the system to maintain versatile event detection capabilities while preventing false behavior analysis, as the event detector can use reflection information to avoid misinterpreting reflection movements as independent object behaviors.
Solution Approach 2:
The system performs preliminary reflection detection and identification before conducting behavior analysis on detected objects. By identifying reflections in advance, the system can then adjust its behavior analysis accordingly, treating reflections appropriately rather than as independent entities. This preliminary action ensures that event detection remains versatile while behavior analysis accuracy is maintained.
Data Source
AI summary
In one embodiment, a device tracks objects in video data captured by one or more cameras in a location. The device represents spatial characteristics of the objects over time as timeseries. The device associates different portions of the timeseries with behavioral regimes of the objects. The device makes a determination that one of the objects is a reflection of another of the objects, based on a correlation between changes of their respective behavioral regimes.